Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
This paper develops a mathematical and computational framework for analyzing the expected performance of Bayesian data fusion, or joint statistical inference, within a sensor network. We use variational techniques to obtain the posterior expectation as the optimal fusion rule under a deterministic constraint and a quad…
Bayesian model fuses multiple classifiers with explicit correlation modeling.
problem Combining outputs of multiple classifiers with explicit correlation.
method Hierarchical Bayesian model with correlated Dirichlet distribution.
result Fused classifier performance can be Bayes optimal even for highly correlated base classifiers.
Paper proposes a new method for Bayesian linear regression using spike-and-slab priors.
problem Identifying predictors with similar relationships in linear regression models.
method Hierarchical Bayesian models with spike-and-slab priors and a Gibbs sampler.
result The proposed method outperforms previous methods in simulations and real data analysis.
The paper analyzes how shared priors affect Bayesian data fusion performance.
problem Effect of shared priors on Bayesian data fusion performance.
method Theoretical analysis using two divergences common in Bayesian inference.
result Theoretical analysis and experimental validation of performance behavior.
Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy. One obstacle in utilizing fusion as a feasible energy source is the stability of the reaction. Ideally, one would have a controller for the reactor that makes actions in response to the current state of t…
BayesIMP combines multiple causal graphs to estimate average treatment effects with uncertainty.
problem Uncertainty quantification in causal inference from multiple datasets.
method Bayesian Interventional Mean Processes (BayesIMP) integrating probabilistic integration and kernel mean embeddings.
result Improvements in average treatment effect estimation over state-of-the-art methods.
We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structur…
Combines multiple asset views with machine learning for better portfolio allocation.
problem Portfolio allocation with multiple uncertain asset views.
method Consistency-based data fusion techniques for combining Black-Litterman model with machine learning predictions.
result Improved portfolio allocation through fusion of multiple view estimates.
New method tackles constrained optimization in multi-fidelity Bayesian optimization.
problem Efficiently identifying feasible regions in constrained optimization problems.
method Proposes CMFBO method with novel acquisition functions.
result Demonstrates effectiveness on synthetic problems and real-world ICF and joint design problems.
PG-EVIKAL refines molecular property predictions using neighbor fusion and evidential neural networks.
problem Improving molecular property predictions using test-time neighbor fusion.
method Adapting evidential neural networks to refine predictions by re-ranking structurally similar neighbors.
result PG-EVIKAL reduces RMSE on 14 out of 16 molecular datasets, improving calibration and sequential refinement.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late fusion. In this paper we focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle th…
New BNN method reduces training time and model size.
problem Overconfident predictions in deep learning models.
method Designing STF-BNN for efficient scaling of BNNs.
result Significantly reduces training time and model size compared to vanilla BNNs.
Adaptive data fusion boosts efficiency in multi-task optimization.
problem Multi-task non-smooth optimization in various fields.
method Adaptive data fusion approach leveraging commonalities among objectives.
result Significant improvements in sample efficiency with sharp statistical guarantees.
Structure learning of Bayesian networks has always been a challenging problem. Nowadays, massive-size networks with thousands or more of nodes but fewer samples frequently appear in many areas. We develop a divide-and-conquer framework, called partition-estimation-fusion (PEF), for structure learning of such big networ…
In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
problem Multi-regime problems in molecular conformation and drug discovery.
method Dirichlet Process Mixture of Gaussian Processes with adaptive hyperparameters and concentration parameters.
result Consistent improvements over state-of-the-art on multi-regime objectives.
Fusion of transformer networks using optimal transport for improved performance.
problem Improving performance of transformer-based models through fusion.
method Exploiting optimal transport for soft alignment of transformer components.
result Consistently outperforms vanilla fusion and individual parent models.
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models f…
Two novel distributed VB algorithms improve Bayesian inference in sensor networks.
problem Efficient inference in Bayesian frameworks for sensor networks.
method Two novel distributed VB algorithms for general Bayesian inference, using stochastic natural gradient and ADMM.
result Distributed algorithms perform nearly as well as centralized ones, demonstrating excellent performance.
Fusion framework improves time series classification across different datasets.
problem Kernel-based methods like Rocket perform inconsistently across datasets.
method Fusion-3 framework that adaptively fuses three representations (Rocket, SAX, SFA) based on dataset properties.
result Fusion-3 framework yields small but consistent average improvements over Rocket on 113 UCR datasets.
Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the r…
Bayesian framework integrates prior and data knowledge for nonlinear dynamical systems.
problem Fusing diverse prior knowledge with data for accurate model learning.
method General-purpose Bayesian inference and learning framework combining explicit and implicit prior knowledge.
result Efficient parameter marginalization and closed-form densities for online and offline inference.
Partial fusion combines neural networks to balance accuracy and efficiency.
problem Balancing accuracy and computational cost in neural networks.
method Extending weight aggregation methods based on neuron-level similarity, using partial optimal transport to match similar neurons.
result Achieves a flexible tradeoff between computational cost and performance.
In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple fe…
We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and in-complete fields from wind tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fus…
Sensor fusion is a key technology that integrates various sensory inputs to allow for robust decision making in many applications such as autonomous driving and robot control. Deep neural networks have been adopted for sensor fusion in a body of recent studies. Among these, the so-called netgated architecture was propo…
CoCoAFusE fuses expert predictions to model complex patterns with interpretability and uncertainty.
problem Modeling complex patterns with interpretability and uncertainty quantification.
method Competitive/Collaborative Fusion of Experts (CoCoAFusE) that fuses expert distributions in addition to mixing.
result CoCoAFusE avoids multimodality artifacts and provides tighter credible bounds on the response variable.
A method for inferring ground-truth signals from degraded sensor data.
problem Inferring ground-truth signals from multiple degraded sensor signals.
method Iterative correction of degraded signals using a Bayesian multi-sensor data fusion method.
result The method effectively infers ground-truth signals from noisy and degraded sensor data.
NAS for financial time series forecasts using chain-structured architectures.
problem Optimizing neural architectures for financial time series forecasting.
method Comparison of three NAS strategies (Bayesian optimization, hyperband, reinforcement learning) on chain-structured search spaces for simple and complex architectures.
result Bayesian optimization and hyperband outperform other strategies, and RNN and 1D CNN perform best among architectures.
Fuses posterior distributions from different datasets using KL divergence.
problem Combining information from multiple datasets with uncertainty.
method Mean field assumption, KL divergence, assign-and-average approach.
result Efficient non-parametric algorithm for fused model computation.
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) …
A new combinatorial approach groups regression coefficients for improved accuracy.
problem Grouping regression coefficients to reveal shared values within groups.
method Introduces L0-Fusion, a combinatorial grouping approach using mixed integer optimization. result L0-Fusion achieves grouping consistency under weak grouping sensitivity conditions. In this paper, we propose to employ a bank of modality-dedicated Convolutional Neural Networks (CNNs), fuse, train, and optimize them together for person classification tasks. A modality-dedicated CNN is used for each modality to extract modality-specific features. We demonstrate that, rather than spatial fusion at the…
A novel one-class classifier fusion method for robust anomaly detection.
problem Fundamental challenges in ensemble-based anomaly detection.
method Locally adaptive learning with dynamic ℓp-norm constraints and interior-point optimization.
result Significantly improved computational efficiency and superior performance across diverse anomaly types.
Bayesian model fuses diverse microbiome data types.
problem Challenges in fusing different types of microbiome data.
method Flexible multinomial-Gaussian generative model with variational EM algorithm.
result Inferred latent variables provide common dimensionality reduction and predictive posterior distribution.
Generative models unify heterogeneous data for multimodal fusion.
problem Learning effective representations of mixed numerical and categorical data.
method Bayesian approach with exponential family distributions and Laplace-Bernstein approximation.
result Generative models enable fusion of multimodal data for various machine learning tasks.
Proposes a new method for subgroup analysis using optimal trees with parameter fusion.
problem Challenges of greedy heuristics and overfitting in tree-based recursive partitioning methods.
method Fused optimal causal tree method leveraging mixed integer optimization (MIO) for globally optimal partitions and parameter fusion.
result Substantial improvement in subgroup discovery accuracy and statistical efficiency.
Proposes a multi-fidelity machine learning strategy integrating low-fidelity deterministic and high-fidelity Bayesian models.
problem Addressing the accuracy-efficiency trade-off in machine learning with scarce high-fidelity data.
method Integrates a non-probabilistic regression model for low-fidelity with a Bayesian model for high-fidelity, trained in a staggered scheme.
result Achieves comparable performance in mean and uncertainty estimation with reduced training time and effective mitigation of overfitting.
In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set of different types of sensors. Exploiting complementary information from different sensors, we show that target detection and classificatio…
GP+ is a Python library for Gaussian process learning.
problem Probabilistic learning and inference for complex models.
method Integration of nonlinear manifold learning with Gaussian processes.
result GP+ enables probabilistic data fusion and inverse parameter estimation.
Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architec…
Proposes Fusion Recurrent Neural Network for sequence data.
problem Improving sequence learning for practical applications.
method Fusion module and Transport module for sequence data.
result Fusion RNN performs comparably to state-of-the-art RNNs.
Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks…
Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify pred…
This work tackles model-based RL by optimizing state-action queries to learn policies with minimal data.
problem Expensive state transitions in practical RL problems limit the use of standard RL algorithms.
method Bayesian optimal experimental design to guide selection of state-action queries.
result Data-efficient RL approach that learns optimal policies with up to 1,000x less data.
Improved speech recognition model with better performance.
problem Speech recognition accuracy on Librispeech.
method Integrates an external language model with an internal LM correction.
result Over 14% relative improvement in performance.